Smart data to improve mobility across territories
Since 2017, we have been designing supervision and information systems for multimodal mobility. We provide real-time and predictive solutions for public transit, road traffic, and cycling to mobility authorities and transport operators.
Today, we are exploring a new dimension: origin-destination (O/D) analysis. This approach helps to better understand the daily movement patterns of residents and adapt the transport offer accordingly.
Origin-destination: a new way of seeing mobility
Mobility authorities typically rely on household surveys or traffic counters to analyze travel behaviors. These methods are often costly and limited to certain modes of transport.
By leveraging data from mobile applications (over 1,000 apps, 40% running in the background), we can detect all types of trips—by car, bike, public transit, or on foot.
A striking example is the province of Cuenca in Spain, where population density is very low (10 people/km²). Even there, our data reveals regular trips—something traditional mobile operator data, often too imprecise, cannot achieve.
Data to redesign transport networks
O/D analyses can help reshape transport offerings. For instance, in the Grand Chalon area, we studied car traffic flows to a business zone to help propose effective carpooling and intercity bus routes, with tailored itineraries and schedules.
An initial analysis using less than one week of data already identified mobility hubs and recurring journeys.
Better insight into intermodal behaviors
Our analysis tool also detects combinations of transport modes. For example, in the Île-de-France region, sequences like walking – bus – train – car can be identified, making it possible to optimize stops and connection times.
In Albertville, public transport, car, bicycle, and pedestrian trips are currently being studied to improve the clarity and efficiency of the network and infrastructure.
Designing efficient feeder solutions
For Metropolitan Regional Express Services (SERM), understanding home-to-station travel is key. We identify the main departure and arrival areas around stations to design relevant feeder bus routes.
The goal is to ensure most journeys to or from the station can be done on foot, making the train a true alternative to driving.
Decision-support tools—not a magical solution
O/D analysis relies on models that incorporate multiple factors: urban density, distance between stops, availability of services, etc.
These tools do not replace urban planning expertise—they complement it. We work with consulting partners who use the data to support informed local decision-making.
AI for local mobility—with common sense
Artificial intelligence is not an end in itself. To us, a tool is only effective if it can be reused by local authorities, helps measure the impact of public policies on mobility, and most importantly, contributes to fairer, greener, and more cost-effective decisions.
Building solutions based on real needs
By working closely with local governments, we continuously improve our products. The city of Le Havre, for example, played a key role in shaping features related to soft mobility and infrastructure constraints.
We design our tools to make life easier for users and territories alike, with one simple ambition: travel better, at lower cost, and with less environmental impact.
Contact us for a demo!